International Journal of Hybrid Intelligent Systems - Volume Pre-press, issue Pre-press

Authors: César, Inês | Pereira, Ivo | Rodrigues, Fátima | Miguéis, Vera | Nicola, Susana | Madureira, Ana

Article Type: Research Article

Abstract: This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use …of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes. Show more

Keywords: Multimodal learning, market analysis, artificial intelligence, machine learning

DOI: 10.3233/HIS-240018

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Gangaramani, Drishti | Londhe, Renuka

Article Type: Research Article

Abstract: Associative rule mining is a technique for discovering common patterns and correlations in data sets from different databases, including relational, transactional and other types of data repositories, such as relational databases. Different types of patterns exist in data mining such as frequent patterns, extended patterns, regular patterns etc. Many searches have focused on finding the frequent patterns and very little work has been carried out on negative or rare patterns. It has also been observed that only those items which are positively correlated(frequent) are been executed by various algorithms but very less attention is been given to negatively correlated items. …Negatively correlated items also called infrequent items are the items which negate with each other. The items which do not satisfy the minimum threshold value generally are always been ignored by many researchers. Mining of Negative association helps in business such as for customer segmentation, in risk management as well as in medical field. So the main aim of writing this paper is to provide a short overview of various research issues involved in finding out positive and negative associations. Show more

Keywords: Association rule mining, negative association mining, item sets, positive association mining, frequent pattern

DOI: 10.3233/HIS-240015

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Subbiah, Priyanga | Tyagi, Amit Kumar | N, Krishnaraj

Article Type: Research Article

Abstract: The identification and severity assessment of plant leaf diseases is crucial to food security and sustainable agriculture. This study shows an innovative way to improve plant leaf disease detection. The recommended method uses Optuna for parameter optimization and the Genetic Algorithm for feature selection to improve plant leaf disease identification. We do this to improve diagnosis accuracy. This method improves classification accuracy and is called ECPLDD-OGA. Modern hyper parameter optimization framework Optuna is employed. This allows classification model parameters to be fine-tuned. A systematic feature selection method is the Genetic Algorithm. It finds the most useful characteristics in the input …dataset. By applying the algorithm on the data. By facilitation, the iterative process helps create a simplified and meaningful subset of features. Contrary to parameter tinkering and feature selection, empirical data suggests that utilizing Optuna and the Genetic Algorithm simultaneously improves disease identification. The updated model recognizes sick plants more accurately and generalizes better. Optimization enabled both gains. The usage of this technology can improve agricultural operations and reduce crop losses by increasing productivity. The present ECPLDD-OGA technique helps integrate hyper parameter tweaking and feature selection into machine learning-based agricultural applications. Show more

Keywords: Optuna algorithm, genetic algorithm, optimization techniques, hyperparameter tuning, hybrid approaches, performance optimization

DOI: 10.3233/HIS-240025

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Chaudhary, Bhawesh K. | Agrawal, Sanjay | Mishro, Pranaba K. | Dora, Lingraj | Mahapatra, Sakambhari | Panda, Rutuparna

Article Type: Research Article

Abstract: Mammogram image analysis is a crucial domain in the image-based diagnosis process. It is a trusted modality of non-invasive imaging for detecting tumour regions in the breast mass. However, poor contrast in the mammogram images is a key challenging issue. To address the issue, a brightness preserving gradient based joint histogram equalization (BPGJHE) method is suggested for enhancing the image quality while restoring the actual brightness and the structural information. The key contributions of the proposed method are (1) preserve the actual brightness of the mammogram images, (2) preserve the multi-scale structural details using an improved gradient filtering approach, (3) …enrich the performance of the histogram equalization approach by incorporating the spatial information in the histogram. The suggested method is assessed using a series of mammogram images from standard datasets. The performance of the suggested method is validated in competence to the cutting-edge schemes. The quantitative assessment is performed using extensive validation metrics. The results indicate the efficacy of the suggested method. Show more

Keywords: Mammogram image analysis, histogram equalization, gradient based filtering

DOI: 10.3233/HIS-240022

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Narayanan, Neethu | K R, Remya | Varghese, Bindiya M.

Article Type: Research Article

Abstract: This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics – accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32% – …and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention. And, this study addresses the critical need for early detection and intervention in autism spectrum disorder (ASD) using machine learning. Specific therapies are needed for ASD, a neurodevelopmental disease that affects social interaction and communication. To find trends in ASD, our research uses a variety of early childhood screening tests as training sets for machine learning algorithms. The methodology that has been suggested utilizes methods of machine learning to compute the ASD spectrum, considering its many expressions. By using multidisciplinary methods and sophisticated screening instruments, we want to create an accurate system for early ASD detection. Algorithmic transparency, data protection, and ethical considerations are essential. This study seeks to build precise instruments for early ASD detection by promoting collaboration between specialists in neurodevelopment, psychology, and machine learning. A robust instrument that enhances the knowledge of medical practitioners is machine learning. Results show how innovation may transform early interventions and help people on the autistic spectrum achieve enhanced results. Show more

Keywords: Autism spectrum disorder (ASD), neurological, machine learning

DOI: 10.3233/HIS-240029

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Rana, Bishwamitra | Maringanti, Hima Bindu

Article Type: Research Article

Abstract: There are many reasons associated with stress, long term stress induces neurological and psychosomatic disorders like hypertension, hypothyroidism, diabetes, anxiety and depression which affect the lifestyle of human beings. Consequently, behavioural activity and action gradually change in their surrounding environment and also perceived by others. In general, stressful respiration is relatively different from normal. To release stress and control all the neuropsychological hormones, multiple activities like playing games, watching a movie, listening to songs and music, etc. or intake of medicine/drugs such as (Allopathic /Homeopathic/Ayurvedic) are used. Medicines can provide easy stress evasion, but relief is only temporary. Thus, yoga …and Sudarshan kriya (SK) meditation is a unique and alternate therapy identified by Gurudev Sri Sri Ravi Shankar by Art of living. It would be a healthy way to get rid of stress in peoples’ lives. Study of long-term effects of (SKY) Sudarshan kriya Yoga before and after and response of the brain regions in experienced (10–15 yrs) practitioners, mediocre (3–5 yrs) and novice(non-practitioners) is the main objective of this work. This study is planned in three phases, the first phase is an experiment on SKY practitioners for more than 10–15 years, in which their (EEG) Electroencephalogram is recorded just after a session of meditation and the common portion of excitation amongst the three subjects is mined and analysed, to draw inferences. This inference would help us draw a conclusion about (BLOC) base level of consciousness considered as benchmark. In the second phase, comparison of benchmark data with the Mediocre (3–5 yrs) measurement and in third phase, benchmark versus Novice data, is done. Next is the phase of interpretation of the response in the form of EEG spectral waves as Type I- 10 to 15 years SKY Practitioners (Superconscious), Type II- SKY practitioners 3 to 5 years (mediocre/semiconscious) and Type. III- Non-practitioner subjects (Novice/Un-conscious). The unconsciousness here means a state of complete unawareness of the self, though conscious of the external, physical world. Thus, power spectrum analysis (PSA) is carried out and frequency of each electrode is computed through segment analysis, Power Spectrum Density (PSD), Correlation coefficient, Mean and Standard Deviation, for finding the level of consciousness. The spectral waveform of these recordings is analysed programmatically using machine learning techniques (used Python Language run on the Jupyter notebook, Spyder, Google colab environment).Frequency analysis results are obtained by placing 21 electrodes in human brain in different lobes that is (Fz, C2, P2, FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, BP4, E.G, T6) those are frequency measuring electrodes/channels placed on the frontal lobe, temporal lobe, parietal lobe and occipital lobe over skull and brainwaves alpha (α )[8–12 Hz], beta (β )[12–16 Hz], delta (δ )[0.5–4 Hz], theta (Θ )[4–8 Hz], gamma (γ )[16–32 Hz] are synthesized. The interpretation of these analyses suggests alternative therapeutic techniques, to improve both mentally and psychologically and thus become socially acceptable. Show more

Keywords: SKY, ND, ML, meditation, stress, LTP, BLOC

DOI: 10.3233/HIS-240032

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Nabajja, Subhashish | Kanojia, Mahendra

Article Type: Research Article

Abstract: Cholangiocarcinoma (CCA) is a type of cancer that forms in the bile duct that carry digestive fluid from the liver. CCA is the primary form of liver cancer that affects population ranging from age 60 to 69 years. CCA is difficult to diagnose at an early stage. Hyperspectral (HS) imaging is an advanced imaging technique that combines spectroscopy with conventional imaging. HS imaging is an emerging field of study which can be used for early CCA detection. HS imaging involves capturing images across various spectral bands, which forms a three-dimensional data cube often called as hyperspectral data cube. In this study, …we have utilized U-Net based models, namely U-Net and DenseUNet were used to perform semantic segmentation on the HS images of CCA tissues. A band selective approach was employed to derive a subset of meaningful bands based on the spectrum plot from the HS image. The HS images are further preprocessed with Principal Component Analysis (PCA). The models were further evaluated by computing the accuracy, AUC (Area under the ROC curve), sensitivity and specificity metrics. The proposed models, namely, U-Net and DenseUNet reported an overall accuracy of 73.47% and 77.09% respectively. The DenseUNet models outperforms the U-Net model on every evaluation metric. The proposed models were also compared with other state-of-the-art (SOTA) models trained on various HS dataset. This study explores the application of HS imaging in carcinoma detection. The findings of this study could be used for further enhancement of the approach. Show more

Keywords: Hyperspectral imaging, U-Net, DenseUNet

DOI: 10.3233/HIS-240024

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: K, Sruthi | M, Meianbu | S R, Naveen | Krishna R, Nidhish

Article Type: Research Article

Abstract: Handwritten number recognition has been extensively studied in the fields of machine literacy and computer vision, with datasets like MNIST serving as benchmarks. However, handwritten Roman numeral recognition presents unique challenges due to the diverse forms and structures of Roman numerals. In this paper, we propose a novel approach that combines Federated Learning with advanced neural network architectures to tackle this challenge effectively. Our methodology involves data acquisition and preprocessing, including the normalization of handwritten number and Roman numeral images. We design a hybrid neural network architecture that integrates Gated Convolutional Neural Networks (CNNs) for pixel-level feature extraction and Bidirectional …Gated Recurrent Units (BGRUs) for sequence modeling. This architecture is essential for handling the complexity of recognizing both image and sequence data. Federated Learning is incorporated into our approach to train the model across multiple decentralized devices or servers while preserving data privacy. This ensures that sensitive handwritten data remains secure throughout the training process. By allowing model updates to be computed locally and aggregated without sharing raw data, Federated Learning maintains privacy and security in distributed learning environments. During training, each device computes gradients based on its local data and shares only the model updates with the central server. The central server aggregates these updates to update the global model, which is then sent back to the participating devices for further refinement. This iterative process continues until the model converges, while metrics such as accuracy, precision, recall, and F1-score are used to evaluate the model’s performance on a separate test dataset. Our approach demonstrates promising results in accurately recognizing both handwritten integers and Roman numerals, even in the presence of noise and variability in writing styles. By combining Federated Learning with advanced neural network architectures, our approach not only achieves state-of-the-art performance but also ensures data privacy and security in distributed learning environments. Show more

Keywords: Gated convolutional neural network, bidirectional gated recurrent unit, feature extraction, federated learning

DOI: 10.3233/HIS-240030

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Acharya, Badal | Parida, Priyadarsan | Panda, Ravi Narayan | Mohapatra, Pradumya

Article Type: Research Article

Abstract: The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also been extensive research on equalizers based on Radial Basis Function Neural Networks (RBFNNs). This study introduces a training methodology centred on the Improved Butterfly Optimization Algorithm (IBOA) for channel equalization using RBFNN. This approach aims to optimize the performance of RBFNN equalizers by leveraging the IBOA algorithm for training. Previous literature primarily approached the equalization problem as an optimization challenge. In contrast, this study addresses it as a classification problem. This …training approach exhibits substantial enhancements compared to conventional metaheuristic algorithms. Show more

Keywords: Channel equalization, RBFNN, butterfly optimization algorithm

DOI: 10.3233/HIS-240020

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Nahak, Pradeep | Pratihar, Dilip Kumar | Deb, Alok Kanti

Article Type: Research Article

Abstract: Automated assessment of tomato crop maturity is vital for improving agricultural productivity and reducing food waste. Traditionally, farmers have relied on visual inspection and manual assessment to predict tomato maturity, which is prone to human error and time-consuming. Computer vision and deep learning automate this process by analysing visual characteristics, enabling data-driven harvest decisions, optimising quality, and reducing waste for sustainable and efficient agriculture. This research demonstrates deep learning models accurately classifying tomato maturity stages using computer vision techniques, utilising a novel dataset of 4,353 tomato images. The Vision Transformer (ViT) model exhibited superior performance in classifying tomatoes into three …ripeness categories (immature, mature, and partially mature), achieving a remarkable testing accuracy of 98.67% and the Convolution neural network (CNN) models, including EfficientNetB1, EfficientNetB5, EfficientNetB7, InceptionV3, ResNet50, and VGG16, achieved testing accuracies of 88.52%, 89.84%, 91.16%, 90.94%, 93.15%, and 92.27%, respectively, when tested with unseen data. ViT significantly surpassed the performance of CNN models. This research highlights the potential for deploying ViT in agricultural environments to monitor tomato maturity stages and packaging facilities smartly. Transformer-based systems could substantially reduce food waste and improve producer profits and productivity by optimising fruit harvest time and sorting decisions. Show more

Keywords: Tomato maturity assessment, vision transformer, computer vision, deep learning, convolution neural network

DOI: 10.3233/HIS-240021

Citation: International Journal of Hybrid Intelligent Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024